Triple
T13052497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coat of arms of Uganda |
E327480
|
entity |
| Predicate | coffeeSymbolizes |
P108473
|
FINISHED |
| Object | cash crops |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: cash crops | Statement: [Coat of arms of Uganda, coffeeSymbolizes, cash crops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coffeeSymbolizes Context triple: [Coat of arms of Uganda, coffeeSymbolizes, cash crops]
-
A.
coffeeDesignation
Indicates that one entity is designated or classified as a particular type, role, or category of coffee in relation to another entity.
-
B.
teaCulture
Indicates the relationship in which practices, rituals, and social norms surrounding the preparation and consumption of tea are shared, expressed, or maintained.
-
C.
coffeeVariety
Indicates a relationship where a specific type or variety of coffee is associated with a coffee-related entity (such as a product, beverage, or plant).
-
D.
coffeeDesignationType
Indicates the specific classification or type designation assigned to a coffee (e.g., by quality, origin, or regulatory category).
-
E.
coffeeBrand
Indicates that one entity is a brand associated with the production or marketing of coffee products for the other entity.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d98a9829b48190b23624b6b3df4600 |
completed | April 10, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69d9803aca4c8190b1015cd159cc47a9 |
completed | April 10, 2026, 10:56 p.m. |
| PDg | Predicate description generation | batch_69d98a9577d081908ddef9ea77e408e2 |
completed | April 10, 2026, 11:41 p.m. |
Created at: April 9, 2026, 8:57 p.m.